Lately, we’ve been doing a lot of code caving at The Engine Co. Expert code explorers, like Fabien Sanglard, have an amazing ability to take very complex codebases and understand their core concepts. They can extend them and make them turn all kinds of tricks.

While you shouldn’t miss Fabien’s analysis of Quake 3, you don’t have to be an expert game developer working with major engines to gain huge benefits from a little code literacy. You are far more likely to work within the confines of a big codebase than you are to be working on a blue sky project with no dependencies or baggage.

Being able to dig through layers of code and understand complex existing systems is a huge benefit to your capabilities as a coder. So let me lay out my guidelines for becoming an awesome code caver able to dig through the muddiest of codebases to get things done. Starting with…

Look Deeper Than The Surface

“But wait,” you say, “I’m a JavaScript coder. My language is so simple and elegant! All I have to know is jQuery and I’m set.”

You poor, poor fool. What you aren’t seeing when you declare your very clever closure-based MVC microframework is the 5 million lines of C++ and 1500 man-years that went into the browser that is running it. The JIT that converts your template parser to machine code so it runs fast. The highly tuned GC that lets you kind of forget about memory management. And the vast cross platform rendering codebase that actually lets you draw “Hello World” and funny mustache pictures on screen.

And that browser sits on top of the operating system, which is ten times bigger in lines of code and years of effort. Now hopefully you don’t have to worry about the operating system. But suppose you have a neat little node.js server, and you run a neat little JS powered web service with it, and you get a neat little denial of service attack from some jerk in Eastern Europe. Now all of a sudden those unimportant details like how the operating system handles sockets become vastly important.

Tools of the Trade

How do you cut through a huge codebase like butter? How can you hone in on the insights that make you look like a genius? How can you do all this without breaking a sweat?

grep to grok

The first essential tool is grep. grep looks for a string in a set of files. There are many equivalents to grep (WinGrep, find-in-files in a favorite IDE, find on Windows/DOS), but the key is you can give it a folder or set of files to search and a regular expression to look for, and get back a list of results and the files that contained them. I like to use find-in-files in Sublime Text
3 for this because I can click search results and jump to them.

Grepping is super powerful. Suppose you get some weird error message – “Floorblop invalid.” – and nothing else from your app or service. You might be left with some questions. What’s a floorblop? Why is it invalid? Well, it might not matter. Just grep the error string and look at the surrounding code to determine a cause. Sometimes errors are generated dynamically so you might have to look for part of the string like ‘ invalid.”‘ if Floorblop was determined at runtime. With a little bit of cleverness, you can almost always reduce the number of potential sites for the error in the codebase to a manageable number – then just inspect the search results manually to find the place triggering the error.

Now you don’t care about the text of the error, but instead can start looking for functions that might be failing and causing the error to be emitted. In effect you can think of the error string as a unique identifier for some situation rather than a human-relevant string. (And in many codebases, error messages are cryptic at best.)

In the course of investigating your error message, you might encounter some variables or functions that seem to be related. Grep comes to the rescue once again: grep for them! Generally symbols are unique in a project, so you can quickly find all the code relating to a variable or function. You can also take advantage of the language’s syntax to help narrow down your results – for instance, in C++ function implementations in a class or namespace often look like this:

void Foo::bar()

So if you want to find the definition of bar, you can grep for “::bar”, or if it’s a global function, use a return type, ie, “void bar” and go right to the definition. Think about fragments that would match lines you want to see – you don’t have to get an exact match, just one that’s close enough you can find what you want quickly.

It should be obvious that these techniques can apply to any language, not just C/C++ – in Ruby you might use “def” in your search strings, in JS you might use “function”, in assembly, colons. The goal is to just filter down the number of results you have to consider, not take them to zero.

Binary Spelunking with strings

It’s easy to think of a binary as an opaque file. So it can be frustrating when you have a codebase with binaries in it. But fear not: DLLs, EXEs, .SOs, and a.out files have a ton of useful information in them. They have to – otherwise the operating system wouldn’t be able to run them!

One of the first tools to pull out is strings. Strings simply scans any binary file looking for bytes that look like they might be a string (displayable ascii characters ending with \0). When it finds a match, it prints it out. You can store the results in a file, or run them through grep or another tool, to help narrow results.

So suppose you have a function for which you cannot find the implementation – but you notice that the codebase includes a binary .so or DLL. You can check if the function is prepackaged in the binary by running strings and grepping for the name. If you get some hits there and nowhere else, you now have a good hint that you might not have all the source code.

Pulling Back the Hood: strace and wireshark

Maybe you’re getting a weird failure when interacting with your app. The first thing to do is establish exactly what’s happening! strace is a super powerful tool on Linux that dumps every system call a program makes. With this, you can see when and what is being read from disk, what network activity is happening, if it’s requesting memory from the system, and so on. All of a sudden a cryptic dynamic linker error will be easy to diagnose – you can see if libraries are being loaded and if symbols are being found or not. Or you can see what OS call is hanging the process. Super useful, if a bit tedious.

On Windows, SysInternals offers similar tools to inspect process activity.

Cross platform Wireshark is a packet sniffer that can really help you understand network activity. Suppose you are getting a cryptic error relating to some HTTP service. You might not be sure if the service is up, if you have a bad header, if the connection is being dropped… Wireshark will let you tell right away if data is making it to the server and what it is exactly, as well as what the server is sending back to you. It will identify things like corrupt TCP packets that are very rare failures but can totally break your app.

Once you’ve established the lowest levels are working properly, it’s easy to move up the stack to HTTP inspection tools like Charles or Telerik Fiddler. These can inspect HTTPS traffic and give you a higher level view of your app’s behavior. Chrome and Firefox also have built in tools that are similar.

Abusing Your Profiler and Debugger

You can also abuse profiling tools like Instruments to find out what an app is doing. On Windows, Windows Performance Analyzer and xperfview are the tools to try first. These tools allow you to attach to a running process on your system and see the callstacks and where time is spent in them.

In other words, they show you HOW the code is running in various situations and which are most important (due to being at top of call stack or being called often). It’s like a self-guided tour through an unknown codebase – how convenient!

You can also attach a debugger and use the time honored trigger of hitting pause a lot to see where time is spent during execution – or stepping through function calls to see what calls what. This is a bit more tedious but also very useful in understanding a large codebase.

Study the Fundamentals

The best way to learn to understand big codebases is to… wait for it… study big codebases. While every project is unique, after a while you start to internalize common layouts and development philosophies. As you learn to recognize patterns and see how bigger systems fit together, you gain building blocks that let you map out unfamiliar systems and build your own systems to higher standards.

A few books I’ve found educational in this area:

Operating Systems Design and Implementation – A walkthrough of Minix, a small self contained POSIX environment. This goes through every detail of a “pocket” operating system and explains it in detail. Great introduction for the next three books…

Mac OS Internals – A great guide to OS X’s structure with detailed examples and code walkthroughs. Understand the fundamentals of OS X while building valuable code analysis skills.

Conclusions: Fighting Dirty & Getting to the Bottom

At the highest level of mastery, you should be prepared to reason about the behavior of any program from its highest levels of UI down to the behavior of the CPU. Every abstraction is leaky, and even though you want to preserve abstractions to keep development manageable, you shouldn’t let that limit your understanding. Get to the bottom of the stack first, then work your way back.

Don’t be afraid to fight dirty. Do you think something is important? Do you have an assumption about how some code works? Try breaking it to see how it fails. You’d be amazed how many problems can be solved by asking “Is it plugged in?”. Use version control or backups to make sure you can safely make dangerous changes and roll back to a safe state.

Finally – optimize for your sanity and development time. Don’t spend 12 hours when you can spend 1. Think about how you can narrow down your problem and check your assumptions quickly so you can focus on gaining knowledge and making the system do what you need. There are a universe of tools to make it possible to wrangle complex systems. Use them!

4 thoughts on “Code Caving”

Pedant alert: you should be aware that persons who regularly visit caves call the activity “caving”, and use the term “spelunker” to mean “someone untrained and unknowledgeable in current exploration techniques.”

Another interesting approach in spelunking is available if you have the source control of the project (over time). Nice to see the hotspots (not hot paths) of an app mainly by the number of commits that have been made against it. I learned about that approach from this book:

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